Intelligent systems have been developed which are intended to behave autonomously, automate tasks in an intelligent manner, and extend human knowledge. These systems are designed and modeled based on essentially three distinct fields of technology known, respectively, as
(1) artificial intelligence (AD);
(2) artificial neural networks (ANNs); and
(3) brain-based devices (BBDs).
The intelligent systems based on AI and ANN include digital computers which are programmed to perform tasks as far ranging as playing chess to robotics. AI algorithms are logic-based and preprogrammed to carry out complex algorithms implemented with detailed software instructions. ANNs are an oversimplified abstraction of biological neurons that do not take into consideration nervous system structure (i.e. neuroanatomy) and often require a supervisory or teacher signal to get desired results. BBDs, on the other hand, are based on different principles and a different approach to the development of intelligent systems.
BBDs are based on fundamental neurobiological principles and are modeled after the brain bases of perception and learning found in living beings. BBDs incorporate a simulated brain or nervous system with detailed neuroanatomy and neural dynamics that control behavior and shape memory. BBDs also have a physical instantiation, called a morphology or phenotype, which allows active sensing and autonomous movement in the environment. BBDs, similar to living beings, organize unlabeled signals they receive from the environment into categories. When a significant environmental event occurs, BBDs, which have a simulated neuronal area called a value system, adapt the device's behavior.
The different principles upon which logic-based intelligent systems and BBDs operate are significant. As powerful as they are, logic-based machines do not effectively cope with novel situations nor process large data sets simultaneously. By their nature, novel situations cannot be programmed beforehand because these typically consist of unexpected and varying numbers of components and contingencies. Furthermore, situations with broad parameters and changing contexts can lead to substantial difficulties in programming. And, many algorithms have poor scaling properties, meaning the time required to run them increases exponentially as the number of input variables grows.
A challenging problem in intelligent systems such as autonomous robotic systems, therefore, is the successful exploration of unknown terrain. Exploration in the real world requires navigation and spatial memory tasks to be solved. However, the memory required to be successful in this task requires features only found in living beings and that are believed to be the hallmark of “episodic” memory, i.e. (1) the ability to put together multi-modal sensory information into coherent patterns, (2) the ability to put together information over time and recall temporal sequences, and (3) the ability to use memory for goal directed behavior. The hippocampus, which is located in the medial temporal lobe of the brain, and which has been well studied clinically and physiologically, is known to be crucial for memory and navigation in humans and animals. Consequently, the hippocampus has inspired prior biologically based navigation systems, some of which are computational hippocampal models and others of which are hippocampal models that have been applied to robots, but both of which have their limitations.
Prior computational hippocampal models have been run as simulations on a computer with virtual inputs. These computational hippocampal models make assumptions and use “a priori” information in order to get the appropriate responses to the inputs. For example, the hippocampal “place” cells (i.e. neurons that are active when the animal is in a specific location of the environment) of these computational models respond to a sensory input combination specifically engineered by the modeler, such as a 2-D (two-dimensional) point in Cartesian space. Part of the reason for these assumptions having been made is due to the computational hippocampal model not being situated in a real environment, thereby necessitating these biases.
Some of the computational hippocampal models have investigated the interaction between the hippocampus and other areas of the brain, such as the neocortex. However, in some the anatomy of these models was very simple and did not truly reflect hippocampal-cortical interactions in a meaningful way. One such model integrates the hippocampal formation with visual and path integration processing that could be thought of as cortical inputs and does make an assumption that path integration is solved by a moving bump of activity that reflects the animal movement on a map of the environment. This would not be feasible if the animal was in a real-world environment. Others have constructed a sophisticated model of the hippocampus with the appropriate connections in the hippocampus proper, with this model having been used to investigate memory conditions and issues. Although this model is quite detailed, the inputs into it are tokens or symbols which have no bearing to the processed multi-modal sensory input that converges on the hippocampus. This model produces an abstract output pattern that is read out as a memory recall. However, it is hard to resolve this response with that of, for example, a rodent where hippocampal responses lead to actual adaptive behavior.
While hippocampus models also have been instantiated on mobile robots, many of these also make assumptions, such as the “a priori” information driving the response of hippocampal “place” cells or of a map that is input to the hippocampus. A few robotics models, which do include a neural simulation controlling the mobile robot in a navigation task, learn the mappings and hippocampal responses by autonomous exploration. One such model was very loosely tied to neurobiology and used learning algorithms similar to what is known as back propagation for learning. This developed a Simultaneous Localization and Mapping algorithm inspired by the rodent hippocampus, called RatSLAM, which is a hybrid between Artificial Intelligence SLAM systems and attractor dynamics thought to be represented in the hippocampus to create map-like representations of the environment. Yet others constructed a robotics model that integrated visual input with a head direction system, in which “place” cells, developed in the hippocampal layer of the model during exploration, and a biologically-based reward system drove learning between the “place” cells and goal-directed behavior. However, some features of the model are not true to the biology. (1) First, when the robotic model decided that a new place had been discovered, a “place” cell was added to a growing hippocampal layer. In a real being such as a rodent, a hippocampal cell can respond to multiple places depending on the context or any combination of inputs. This flexibility makes the hippocampus a multi-purpose memory map as opposed to a specialized positioning system. Also, hippocampal cells are not added on an as needed basis. (2) Second, the robotic model was feedforward and did not take into consideration the intrinsic and extrinsic looping that is a feature of the hippocampus. In yet another system, there was built a hippocampal neuroanatomy and a biologically-based goal system, which was tested on a mobile robot. However, “place” cells were artificial in the sense that the responses were designed to uniformly cover a grid of a controlled environment. The reward learning was used to build a cognitive map between these places. Moreover, although much of the details found in the hippocampus and the surrounding areas were included in the model, information flowed in a purely feedforward fashion through the model and did not loop back through the entorhinal cortex and then on to the neocortex.
Over ten years ago, a statistical framework for simultaneously creating maps while localizing the robot's position was developed, which has been commonly referred to as SLAM (Simultaneous Localization and Mapping). Since that time, the field of robotic mapping has been dominated by probabilistic techniques. The most popular is the estimation theoretic or Kalman filter based approach because it directly provides both a recursive solution to the navigation problem and a means of computing consistent estimates for vehicle and landmark locations based on statistical models of vehicle motion and landmark observations. These robotic approaches typically measure the distance to landmarks by laser range finders, sonar, or radar to create a map of landmarks and simultaneously estimate the position of the robot. These techniques have been very successful in creating maps for robots in certain office environments, in outdoor environments, and for unmanned aerial vehicles. However, these techniques have not addressed the problem of recognizing objects or situations and taking the appropriate actions, i.e. navigating.